Abstract 92: Deploying spatial transcriptomics to inform intratumoral heterogeneity in late-stage uveal melanoma leveraging advanced preclinical modeling and clinical samples
Authors: Sanjana Srinivasan, Johnathon Rose, Joseph R. Daniele, Michael Peoples, Meng He, Rosalba Minelli, Chiu-Yi Liu, Jason Gay, Melinda Soeung, Khalida Wani, Luigi Perelli, Dmitriy Loza, Ningping Feng, Christopher P. Vellano, Joseph Marszalek, Giulio F. Draetta, P. Andrew Futreal, Scott E. Woodman, Alexander J. Lazar, Timothy Heffernan, Alessandro Carugo, Giannicola Genovese, Virginia Giuliani
Journal: Cancer Research (2023)

Abstract Uveal Melanoma (UM) is a rare tumor characterized by mutually-exclusive activating mutations in GNAQ or GNA11, followed by a mutation event in BAP1, SF3B1 or EIF1AX. Notably, a large subset of patients present with a unique chromosome 3 copy-loss event, which is highly associated with metastatic progression in late-stage disease. To date, the biology underlying the causes and consequences of this unique copy-loss event (Monosomy 3 or M3), and its role in promoting metastases remains largely unexplored. This can be attributed to both the lack of preclinical models and in-depth characterization of paired primary and metastatic tumors in patients. To address this, we derived a preclinical isogenic model of chromosome 3 copy-loss through CRISPR-based centromere targeting in a well characterized Disomy 3 UM cell line. Isogenic Disomy (D3) and Monosomy (M3) clones derived from these efforts have enabled us to develop patient derived xenograft (PDX) models to compare D3 and M3 behavior in paired, in vivo, primary and metastatic settings. This modeling has enabled us to employ spatial transcriptomics in a PDX setting to characterize gene expression signatures across unique D3 and M3 UM clones. Investigation of metastatic UM tumor heterogeneity in our models enables us to characterize unique features of phenotypically transformed clones (e.g. depigmentation and growth advantage). We have also adapted existing methodology to infer chromosomal copy number events from spatial transcriptomics data to our PDX system, where we are able to overcome the lack of same-species microenvironment controls. Our methodology allows for deep characterization of sub-clonal heterogeneity in both D3 and M3 settings and informs on the unique biology underlying invasiveness and outgrowth of M3 tumors. Finally, we complement our preclinical analyses with an investigation of spatial heterogeneity in a patient cohort of paired primary and metastatic tumors. More broadly, comparing these D3 vs. M3 signatures provides an opportunity to expand on our knowledge of metastatic disease drivers and derive prognostic signatures associated with poor survival. Citation Format: Sanjana Srinivasan, Johnathon Rose, Joseph R. Daniele, Michael Peoples, Meng He, Rosalba Minelli, Chiu-Yi Liu, Jason Gay, Melinda Soeung, Khalida Wani, Luigi Perelli, Dmitriy Loza, Ningping Feng, Christopher P. Vellano, Joseph Marszalek, Giulio F. Draetta, P. Andrew Futreal, Scott E. Woodman, Alexander J. Lazar, Timothy Heffernan, Alessandro Carugo, Giannicola Genovese, Virginia Giuliani. Deploying spatial transcriptomics to inform intratumoral heterogeneity in late-stage uveal melanoma leveraging advanced preclinical modeling and clinical samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 92.